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首页> 外文期刊>Journal of supercomputing >Multi-level clustering protocol for load-balanced and scalable clustering in large-scale wireless sensor networks
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Multi-level clustering protocol for load-balanced and scalable clustering in large-scale wireless sensor networks

机译：大型无线传感器网络中负载平衡和可扩展群集的多级聚类协议

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摘要

The advent of wireless sensor networks (WSNs) has revolutionized the field of smart applications. In order to improve the performance of WSNs, refinement of clustering and routing protocols can make a vast difference. Existing classical and evolutionary optimization technique-based protocols have high computational complexity since clustering and routing problems are solved separately. Moreover, these protocols suffer from hot-spot problem due to uneven formation of clusters. In this paper, we propose a multi-level clustering protocol (MLCP) for energy-efficient data gathering in large-scale WSNs. Additionally, a hierarchical clustering architecture is designed in MLCP to jointly solve the problems of clustering and routing. Further, for the purpose of cluster head selection, a hybrid dragonfly algorithm-based particle swarm optimization technique is proposed which combines the exploration and exploitation capabilities of dragonfly algorithm and particle swarm optimization, respectively. MLCP considers intra-cluster distance, node degree and inter-cluster distance for the formation of scalable, load-balanced and energy-efficient clusters. To demonstrate the full potential of MLCP, network simulations have been carried out in diverse network conditions. MLCP has shown up to 90% increase in the network lifetime and an improvement of 19.36% in conservation of energy in comparison with the competent protocols. The comparison of obtained results with state-of-the-art clustering protocols clearly establishes the superiority of MLCP in achieving load-balanced, scalable and energy-efficient clustering.
机译：无线传感器网络（WSNS）的出现彻底改变了智能应用领域。为了提高WSN的性能，聚类和路由协议的细化可以产生巨大差异。基于经典和进化优化技术的协议具有高计算复杂性，因为聚类和路由问题是单独解决的。此外，由于簇形成不均匀，这些方案遭受热点问题。在本文中，我们提出了一种多级聚类协议（MLCP），用于大规模WSN的节能数据。此外，分层聚类架构是在MLCP中设计的，以共同解决聚类和路由的问题。此外，为了群集头选择的目的，提出了一种混合蜻蜓算法的粒子群优化技术，其分别结合了蜻蜓算法和粒子群优化的探索和开发能力。 MLCP考虑集簇内距离，节点度和群集间距离，用于形成可扩展，负载平衡和节能群集。为了证明MLCP的全部潜力，在不同的网络条件下已经进行了网络仿真。与主管议定书相比，MLCP展示网络寿命增长高达90％，增长了19.36％，以便能量保护。与最先进的聚类协议的获得结果的比较明确建立了MLCP在实现负载平衡，可扩展和节能的聚类方面的优越性。

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